Overview

Dataset statistics

Number of variables24
Number of observations1307
Missing cells4327
Missing cells (%)13.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory245.2 KiB
Average record size in memory192.1 B

Variable types

Numeric19
Categorical4
Boolean1

Alerts

Aneu_neck is highly overall correlated with Aneu_width and 13 other fieldsHigh correlation
Aneu_width is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
Aneu_depth is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
Aneu_height is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
Aneu_volume is highly overall correlated with Aneu_depthHigh correlation
coil_length1 is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
coil_size1 is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
coil_length2 is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
coil_size2 is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
coil_length3 is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
coil_size3 is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
coil_length4 is highly overall correlated with Aneu_neck and 13 other fieldsHigh correlation
coil_size4 is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
coil_length5 is highly overall correlated with Aneu_neck and 13 other fieldsHigh correlation
coil_size5 is highly overall correlated with Aneu_neck and 13 other fieldsHigh correlation
coil_count is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
Aneu_width_label is highly overall correlated with Aneu_width and 12 other fieldsHigh correlation
Is_bleb is highly imbalanced (68.3%)Imbalance
Aneu_depth has 1194 (91.4%) missing valuesMissing
VER has 1131 (86.5%) missing valuesMissing
coil_length2 has 53 (4.1%) missing valuesMissing
coil_size2 has 53 (4.1%) missing valuesMissing
coil_length3 has 156 (11.9%) missing valuesMissing
coil_size3 has 156 (11.9%) missing valuesMissing
coil_length4 has 315 (24.1%) missing valuesMissing
coil_size4 has 315 (24.1%) missing valuesMissing
coil_length5 has 477 (36.5%) missing valuesMissing
coil_size5 has 477 (36.5%) missing valuesMissing
ID has unique valuesUnique
Aneu_volume has 1194 (91.4%) zerosZeros

Reproduction

Analysis started2023-09-16 06:33:12.712777
Analysis finished2023-09-16 06:34:21.825445
Duration1 minute and 9.11 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct1307
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3735.2762
Minimum3026
Maximum4434
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:21.921467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3026
5-th percentile3101.3
Q13388.5
median3731
Q34090.5
95-th percentile4356.7
Maximum4434
Range1408
Interquartile range (IQR)702

Descriptive statistics

Standard deviation404.39762
Coefficient of variation (CV)0.10826445
Kurtosis-1.2080795
Mean3735.2762
Median Absolute Deviation (MAD)352
Skewness-0.017634452
Sum4882006
Variance163537.44
MonotonicityStrictly increasing
2023-09-16T15:34:22.289146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3026 1
 
0.1%
3962 1
 
0.1%
3981 1
 
0.1%
3980 1
 
0.1%
3979 1
 
0.1%
3978 1
 
0.1%
3977 1
 
0.1%
3976 1
 
0.1%
3975 1
 
0.1%
3974 1
 
0.1%
Other values (1297) 1297
99.2%
ValueCountFrequency (%)
3026 1
0.1%
3027 1
0.1%
3030 1
0.1%
3031 1
0.1%
3032 1
0.1%
3034 1
0.1%
3035 1
0.1%
3037 1
0.1%
3038 1
0.1%
3039 1
0.1%
ValueCountFrequency (%)
4434 1
0.1%
4433 1
0.1%
4432 1
0.1%
4431 1
0.1%
4430 1
0.1%
4427 1
0.1%
4425 1
0.1%
4424 1
0.1%
4423 1
0.1%
4422 1
0.1%

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
woman
957 
man
350 

Length

Max length5
Median length5
Mean length4.4644223
Min length3

Characters and Unicode

Total characters5835
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwoman
2nd rowwoman
3rd rowwoman
4th rowman
5th rowwoman

Common Values

ValueCountFrequency (%)
woman 957
73.2%
man 350
 
26.8%

Length

2023-09-16T15:34:22.490627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:34:22.659274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
woman 957
73.2%
man 350
 
26.8%

Most occurring characters

ValueCountFrequency (%)
m 1307
22.4%
a 1307
22.4%
n 1307
22.4%
w 957
16.4%
o 957
16.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5835
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 1307
22.4%
a 1307
22.4%
n 1307
22.4%
w 957
16.4%
o 957
16.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5835
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 1307
22.4%
a 1307
22.4%
n 1307
22.4%
w 957
16.4%
o 957
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1307
22.4%
a 1307
22.4%
n 1307
22.4%
w 957
16.4%
o 957
16.4%

Age
Real number (ℝ)

Distinct63
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.638868
Minimum22
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:22.820711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile40.3
Q152
median61
Q370
95-th percentile78
Maximum89
Range67
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.691332
Coefficient of variation (CV)0.19280261
Kurtosis-0.40349029
Mean60.638868
Median Absolute Deviation (MAD)9
Skewness-0.26061896
Sum79255
Variance136.68725
MonotonicityNot monotonic
2023-09-16T15:34:23.014300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 52
 
4.0%
70 47
 
3.6%
64 47
 
3.6%
60 44
 
3.4%
50 44
 
3.4%
58 43
 
3.3%
71 42
 
3.2%
62 41
 
3.1%
63 39
 
3.0%
67 39
 
3.0%
Other values (53) 869
66.5%
ValueCountFrequency (%)
22 1
 
0.1%
25 2
0.2%
29 4
0.3%
30 1
 
0.1%
31 1
 
0.1%
32 2
0.2%
33 2
0.2%
34 4
0.3%
35 4
0.3%
36 3
0.2%
ValueCountFrequency (%)
89 2
 
0.2%
88 1
 
0.1%
87 4
 
0.3%
86 1
 
0.1%
85 2
 
0.2%
84 4
 
0.3%
83 11
0.8%
82 4
 
0.3%
81 6
0.5%
80 6
0.5%

Aneu_location
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
ICA
733 
ACA
266 
MCA
162 
BA
110 
VA
 
36

Length

Max length3
Median length3
Mean length2.8882938
Min length2

Characters and Unicode

Total characters3775
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowICA
2nd rowBA
3rd rowICA
4th rowACA
5th rowICA

Common Values

ValueCountFrequency (%)
ICA 733
56.1%
ACA 266
 
20.4%
MCA 162
 
12.4%
BA 110
 
8.4%
VA 36
 
2.8%

Length

2023-09-16T15:34:23.204268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:34:23.383561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ica 733
56.1%
aca 266
 
20.4%
mca 162
 
12.4%
ba 110
 
8.4%
va 36
 
2.8%

Most occurring characters

ValueCountFrequency (%)
A 1573
41.7%
C 1161
30.8%
I 733
19.4%
M 162
 
4.3%
B 110
 
2.9%
V 36
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3775
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1573
41.7%
C 1161
30.8%
I 733
19.4%
M 162
 
4.3%
B 110
 
2.9%
V 36
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3775
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1573
41.7%
C 1161
30.8%
I 733
19.4%
M 162
 
4.3%
B 110
 
2.9%
V 36
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1573
41.7%
C 1161
30.8%
I 733
19.4%
M 162
 
4.3%
B 110
 
2.9%
V 36
 
1.0%

Aneu_neck
Real number (ℝ)

Distinct366
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2382249
Minimum0.99
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:23.564831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile1.593
Q12.31
median3
Q33.88
95-th percentile5.62
Maximum15
Range14.01
Interquartile range (IQR)1.57

Descriptive statistics

Standard deviation1.3828392
Coefficient of variation (CV)0.42703618
Kurtosis9.2772898
Mean3.2382249
Median Absolute Deviation (MAD)0.75
Skewness2.0291282
Sum4232.36
Variance1.9122443
MonotonicityNot monotonic
2023-09-16T15:34:23.759421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 100
 
7.7%
2.5 83
 
6.4%
2 74
 
5.7%
3.5 68
 
5.2%
4 49
 
3.7%
4.5 31
 
2.4%
5 25
 
1.9%
1.5 24
 
1.8%
5.5 13
 
1.0%
2.4 11
 
0.8%
Other values (356) 829
63.4%
ValueCountFrequency (%)
0.99 1
 
0.1%
1 5
0.4%
1.06 1
 
0.1%
1.07 1
 
0.1%
1.1 2
 
0.2%
1.13 1
 
0.1%
1.2 4
0.3%
1.26 1
 
0.1%
1.31 1
 
0.1%
1.32 1
 
0.1%
ValueCountFrequency (%)
15 1
 
0.1%
14.5 1
 
0.1%
10.2 1
 
0.1%
9.8 1
 
0.1%
9 5
0.4%
8.92 1
 
0.1%
8.5 2
 
0.2%
8 3
0.2%
7.9 1
 
0.1%
7.64 1
 
0.1%

Aneu_width
Real number (ℝ)

Distinct452
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7175126
Minimum1
Maximum40.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:23.973617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.215
Q13.1
median4.2
Q35.5
95-th percentile8.988
Maximum40.47
Range39.47
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation2.4593474
Coefficient of variation (CV)0.52132292
Kurtosis38.148378
Mean4.7175126
Median Absolute Deviation (MAD)1.19
Skewness3.8958039
Sum6165.789
Variance6.0483898
MonotonicityNot monotonic
2023-09-16T15:34:24.176188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 69
 
5.3%
4 61
 
4.7%
3.5 57
 
4.4%
4.5 56
 
4.3%
5 49
 
3.7%
6 33
 
2.5%
2.5 28
 
2.1%
5.5 27
 
2.1%
7 24
 
1.8%
2 22
 
1.7%
Other values (442) 881
67.4%
ValueCountFrequency (%)
1 1
 
0.1%
1.2 1
 
0.1%
1.3 1
 
0.1%
1.43 1
 
0.1%
1.5 3
0.2%
1.55 1
 
0.1%
1.56 1
 
0.1%
1.58 1
 
0.1%
1.63 1
 
0.1%
1.66 1
 
0.1%
ValueCountFrequency (%)
40.47 1
0.1%
19 2
0.2%
18 1
0.1%
17.5 1
0.1%
15.5 2
0.2%
15.4 1
0.1%
14.81 1
0.1%
14.77 1
0.1%
14.76 1
0.1%
14 1
0.1%

Aneu_depth
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)71.7%
Missing1194
Missing (%)91.4%
Infinite0
Infinite (%)0.0%
Mean5.0766372
Minimum2.215
Maximum12.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:24.396454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.215
5-th percentile2.79
Q13.6
median4.65
Q35.9
95-th percentile8.437
Maximum12.75
Range10.535
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation2.0055782
Coefficient of variation (CV)0.39506038
Kurtosis2.6975872
Mean5.0766372
Median Absolute Deviation (MAD)1.15
Skewness1.4203351
Sum573.66
Variance4.0223439
MonotonicityNot monotonic
2023-09-16T15:34:24.597459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 5
 
0.4%
4.25 5
 
0.4%
5 4
 
0.3%
4.5 4
 
0.3%
4.75 3
 
0.2%
6 3
 
0.2%
3 3
 
0.2%
3.75 2
 
0.2%
3.3 2
 
0.2%
4.6 2
 
0.2%
Other values (71) 80
 
6.1%
(Missing) 1194
91.4%
ValueCountFrequency (%)
2.215 1
 
0.1%
2.25 1
 
0.1%
2.5 1
 
0.1%
2.65 2
0.2%
2.775 1
 
0.1%
2.8 1
 
0.1%
2.82 1
 
0.1%
2.95 1
 
0.1%
3 3
0.2%
3.05 1
 
0.1%
ValueCountFrequency (%)
12.75 1
0.1%
12.4 1
0.1%
10.725 1
0.1%
10.45 1
0.1%
9.5 1
0.1%
8.5 1
0.1%
8.395 1
0.1%
8.38 1
0.1%
8 1
0.1%
7.65 1
0.1%

Aneu_height
Real number (ℝ)

Distinct442
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9334124
Minimum1.5
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:24.792577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.5
Q13.5
median4.5
Q35.82
95-th percentile9
Maximum18
Range16.5
Interquartile range (IQR)2.32

Descriptive statistics

Standard deviation2.0937057
Coefficient of variation (CV)0.42439301
Kurtosis5.2422954
Mean4.9334124
Median Absolute Deviation (MAD)1.03
Skewness1.7729331
Sum6447.97
Variance4.3836037
MonotonicityNot monotonic
2023-09-16T15:34:24.984946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 65
 
5.0%
5 62
 
4.7%
4.5 55
 
4.2%
3.5 50
 
3.8%
3 43
 
3.3%
5.5 42
 
3.2%
6 36
 
2.8%
7 34
 
2.6%
6.5 18
 
1.4%
8 17
 
1.3%
Other values (432) 885
67.7%
ValueCountFrequency (%)
1.5 1
 
0.1%
1.53 1
 
0.1%
1.55 1
 
0.1%
1.59 1
 
0.1%
1.7 2
 
0.2%
1.75 1
 
0.1%
1.85 1
 
0.1%
1.92 1
 
0.1%
2 6
0.5%
2.01 1
 
0.1%
ValueCountFrequency (%)
18 1
0.1%
17.5 1
0.1%
16 1
0.1%
15.91 1
0.1%
15.5 1
0.1%
15.2 1
0.1%
15 1
0.1%
13.63 1
0.1%
13.5 2
0.2%
13 1
0.1%

Aneu_volume
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct104
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0289067
Minimum0
Maximum1080.945
Zeros1194
Zeros (%)91.4%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:25.196775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile42.823361
Maximum1080.945
Range1080.945
Interquartile range (IQR)0

Descriptive statistics

Standard deviation56.30164
Coefficient of variation (CV)6.2357095
Kurtosis196.47732
Mean9.0289067
Median Absolute Deviation (MAD)0
Skewness12.481896
Sum11800.781
Variance3169.8746
MonotonicityNot monotonic
2023-09-16T15:34:25.398369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1194
91.4%
21.98 4
 
0.3%
14.13 3
 
0.2%
40.16845 2
 
0.2%
109.9 2
 
0.2%
47.1 2
 
0.2%
55.93125 2
 
0.2%
65.41666667 2
 
0.2%
300.8316517 1
 
0.1%
15.64949624 1
 
0.1%
Other values (94) 94
 
7.2%
ValueCountFrequency (%)
0 1194
91.4%
5.633631 1
 
0.1%
5.8875 1
 
0.1%
7.33975 1
 
0.1%
9.458203333 1
 
0.1%
9.56915 1
 
0.1%
11.12188 1
 
0.1%
11.1335294 1
 
0.1%
11.71829474 1
 
0.1%
13.40047333 1
 
0.1%
ValueCountFrequency (%)
1080.945 1
0.1%
992.5435333 1
0.1%
633.0654402 1
0.1%
596.1028333 1
0.1%
437.5066667 1
0.1%
311.3833333 1
0.1%
307.0434682 1
0.1%
300.8316517 1
0.1%
267.9466667 1
0.1%
234.20475 1
0.1%

Adj_tech
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
Stent assist
471 
BAT
454 
Simple
304 
Double cathe
77 
Triple cathe
 
1

Length

Max length12
Median length6
Mean length7.4781943
Min length3

Characters and Unicode

Total characters9774
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowBAT
2nd rowSimple
3rd rowBAT
4th rowSimple
5th rowSimple

Common Values

ValueCountFrequency (%)
Stent assist 471
36.0%
BAT 454
34.7%
Simple 304
23.3%
Double cathe 77
 
5.9%
Triple cathe 1
 
0.1%

Length

2023-09-16T15:34:25.735112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:34:25.906499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
stent 471
25.4%
assist 471
25.4%
bat 454
24.5%
simple 304
16.4%
cathe 78
 
4.2%
double 77
 
4.1%
triple 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 1491
15.3%
s 1413
14.5%
e 931
9.5%
i 776
7.9%
S 775
7.9%
549
 
5.6%
a 549
 
5.6%
n 471
 
4.8%
T 455
 
4.7%
A 454
 
4.6%
Other values (11) 1910
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7010
71.7%
Uppercase Letter 2215
 
22.7%
Space Separator 549
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1491
21.3%
s 1413
20.2%
e 931
13.3%
i 776
11.1%
a 549
 
7.8%
n 471
 
6.7%
l 382
 
5.4%
p 305
 
4.4%
m 304
 
4.3%
c 78
 
1.1%
Other values (5) 310
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
S 775
35.0%
T 455
20.5%
A 454
20.5%
B 454
20.5%
D 77
 
3.5%
Space Separator
ValueCountFrequency (%)
549
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9225
94.4%
Common 549
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1491
16.2%
s 1413
15.3%
e 931
10.1%
i 776
8.4%
S 775
8.4%
a 549
 
6.0%
n 471
 
5.1%
T 455
 
4.9%
A 454
 
4.9%
B 454
 
4.9%
Other values (10) 1456
15.8%
Common
ValueCountFrequency (%)
549
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1491
15.3%
s 1413
14.5%
e 931
9.5%
i 776
7.9%
S 775
7.9%
549
 
5.6%
a 549
 
5.6%
n 471
 
4.8%
T 455
 
4.7%
A 454
 
4.6%
Other values (11) 1910
19.5%

Is_bleb
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
1232 
True
 
75
ValueCountFrequency (%)
False 1232
94.3%
True 75
 
5.7%
2023-09-16T15:34:26.076773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

VER
Real number (ℝ)

Distinct146
Distinct (%)83.0%
Missing1131
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean34.154716
Minimum11.9
Maximum70.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:26.227517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11.9
5-th percentile18.925
Q127.11
median32.5
Q339.01
95-th percentile55.2075
Maximum70.58
Range58.68
Interquartile range (IQR)11.9

Descriptive statistics

Standard deviation10.999226
Coefficient of variation (CV)0.32204121
Kurtosis1.1156554
Mean34.154716
Median Absolute Deviation (MAD)6.1
Skewness0.82746492
Sum6011.23
Variance120.98297
MonotonicityNot monotonic
2023-09-16T15:34:26.419788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.8 4
 
0.3%
25.2 3
 
0.2%
24.5 3
 
0.2%
36.5 3
 
0.2%
34.7 2
 
0.2%
32 2
 
0.2%
34.1 2
 
0.2%
28.6 2
 
0.2%
26.5 2
 
0.2%
32.5 2
 
0.2%
Other values (136) 151
 
11.6%
(Missing) 1131
86.5%
ValueCountFrequency (%)
11.9 1
0.1%
12.89 1
0.1%
12.9 1
0.1%
14.2 1
0.1%
14.21 1
0.1%
15 1
0.1%
16.1 1
0.1%
17.3 1
0.1%
17.5 1
0.1%
19.4 1
0.1%
ValueCountFrequency (%)
70.58 1
0.1%
69.7 1
0.1%
66.4 1
0.1%
64.5 1
0.1%
60.8 1
0.1%
60.4 1
0.1%
59.5 1
0.1%
58.8 1
0.1%
56.7 1
0.1%
54.71 1
0.1%

coil_length1
Real number (ℝ)

Distinct45
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.494568
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:26.622184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q315
95-th percentile30
Maximum60
Range59
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.0680439
Coefficient of variation (CV)0.70190059
Kurtosis3.8300638
Mean11.494568
Median Absolute Deviation (MAD)2
Skewness1.7886901
Sum15023.4
Variance65.093332
MonotonicityNot monotonic
2023-09-16T15:34:26.808375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
8 251
19.2%
6 216
16.5%
10 186
14.2%
15 131
10.0%
4 121
9.3%
20 94
 
7.2%
30 70
 
5.4%
12 39
 
3.0%
3 31
 
2.4%
25 21
 
1.6%
Other values (35) 147
11.2%
ValueCountFrequency (%)
1 1
 
0.1%
2 18
 
1.4%
2.5 2
 
0.2%
3 31
 
2.4%
3.5 4
 
0.3%
4 121
9.3%
4.5 3
 
0.2%
5 4
 
0.3%
5.4 3
 
0.2%
6 216
16.5%
ValueCountFrequency (%)
60 1
 
0.1%
50 3
 
0.2%
47 2
 
0.2%
45 2
 
0.2%
43 3
 
0.2%
40 7
 
0.5%
36 2
 
0.2%
33 1
 
0.1%
30 70
5.4%
29 3
 
0.2%

coil_size1
Real number (ℝ)

Distinct22
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7042846
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:26.989984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile9
Maximum20
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4135839
Coefficient of variation (CV)0.51306078
Kurtosis7.8934195
Mean4.7042846
Median Absolute Deviation (MAD)1
Skewness2.2464151
Sum6148.5
Variance5.8253874
MonotonicityNot monotonic
2023-09-16T15:34:27.148638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 245
18.7%
4 235
18.0%
5 217
16.6%
6 134
10.3%
3.5 98
 
7.5%
2.5 87
 
6.7%
7 76
 
5.8%
2 61
 
4.7%
8 44
 
3.4%
10 23
 
1.8%
Other values (12) 87
 
6.7%
ValueCountFrequency (%)
1 5
 
0.4%
1.5 10
 
0.8%
2 61
 
4.7%
2.5 87
 
6.7%
3 245
18.7%
3.5 98
 
7.5%
4 235
18.0%
4.5 14
 
1.1%
5 217
16.6%
6 134
10.3%
ValueCountFrequency (%)
20 3
 
0.2%
18 2
 
0.2%
16 6
 
0.5%
15 2
 
0.2%
14 6
 
0.5%
13 1
 
0.1%
12 14
1.1%
11 1
 
0.1%
10 23
1.8%
9 23
1.8%

coil_length2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)2.9%
Missing53
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean7.907496
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:27.323932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q39
95-th percentile23.35
Maximum57
Range56
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.9031569
Coefficient of variation (CV)0.87298898
Kurtosis8.2469475
Mean7.907496
Median Absolute Deviation (MAD)2
Skewness2.5262941
Sum9916
Variance47.653575
MonotonicityNot monotonic
2023-09-16T15:34:27.493306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
4 262
20.0%
6 205
15.7%
8 192
14.7%
3 137
10.5%
10 113
8.6%
2 107
8.2%
15 55
 
4.2%
30 38
 
2.9%
20 36
 
2.8%
12 22
 
1.7%
Other values (26) 87
 
6.7%
(Missing) 53
 
4.1%
ValueCountFrequency (%)
1 9
 
0.7%
2 107
8.2%
2.5 1
 
0.1%
3 137
10.5%
3.5 4
 
0.3%
4 262
20.0%
4.5 1
 
0.1%
5 9
 
0.7%
5.4 2
 
0.2%
6 205
15.7%
ValueCountFrequency (%)
57 1
 
0.1%
50 3
 
0.2%
40 3
 
0.2%
36 2
 
0.2%
31 1
 
0.1%
30 38
2.9%
27 2
 
0.2%
26 1
 
0.1%
25 6
 
0.5%
24 6
 
0.5%

coil_size2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)1.7%
Missing53
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean3.6463317
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:27.657894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12
median3
Q34
95-th percentile8
Maximum25
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3358134
Coefficient of variation (CV)0.64059266
Kurtosis13.342686
Mean3.6463317
Median Absolute Deviation (MAD)1
Skewness2.8341321
Sum4572.5
Variance5.456024
MonotonicityNot monotonic
2023-09-16T15:34:27.821575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 246
18.8%
3 220
16.8%
4 199
15.2%
2.5 152
11.6%
5 108
8.3%
1.5 63
 
4.8%
6 62
 
4.7%
1 48
 
3.7%
3.5 40
 
3.1%
7 29
 
2.2%
Other values (11) 87
 
6.7%
(Missing) 53
 
4.1%
ValueCountFrequency (%)
1 48
 
3.7%
1.5 63
 
4.8%
2 246
18.8%
2.5 152
11.6%
3 220
16.8%
3.5 40
 
3.1%
4 199
15.2%
4.5 10
 
0.8%
5 108
8.3%
6 62
 
4.7%
ValueCountFrequency (%)
25 1
 
0.1%
18 4
 
0.3%
16 3
 
0.2%
15 1
 
0.1%
14 5
 
0.4%
12 5
 
0.4%
11 2
 
0.2%
10 11
0.8%
9 20
1.5%
8 25
1.9%

coil_length3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)2.3%
Missing156
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean6.4339705
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:27.989870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q38
95-th percentile20
Maximum60
Range59
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.1216992
Coefficient of variation (CV)0.95146524
Kurtosis12.773915
Mean6.4339705
Median Absolute Deviation (MAD)2
Skewness3.0572842
Sum7405.5
Variance37.475202
MonotonicityNot monotonic
2023-09-16T15:34:28.146570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4 249
19.1%
2 200
15.3%
3 174
13.3%
6 165
12.6%
8 145
11.1%
10 62
 
4.7%
15 38
 
2.9%
30 26
 
2.0%
20 21
 
1.6%
12 15
 
1.1%
Other values (17) 56
 
4.3%
(Missing) 156
11.9%
ValueCountFrequency (%)
1 12
 
0.9%
2 200
15.3%
3 174
13.3%
3.5 1
 
0.1%
4 249
19.1%
4.5 2
 
0.2%
5 7
 
0.5%
6 165
12.6%
7 5
 
0.4%
7.5 2
 
0.2%
ValueCountFrequency (%)
60 1
 
0.1%
50 1
 
0.1%
40 2
 
0.2%
31 1
 
0.1%
30 26
2.0%
25 9
 
0.7%
24 4
 
0.3%
22 1
 
0.1%
20 21
1.6%
17 2
 
0.2%

coil_size3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)1.7%
Missing156
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean3.157689
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:28.307491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2.5
Q34
95-th percentile7
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1355438
Coefficient of variation (CV)0.67629961
Kurtosis12.502187
Mean3.157689
Median Absolute Deviation (MAD)0.5
Skewness2.8663567
Sum3634.5
Variance4.5605474
MonotonicityNot monotonic
2023-09-16T15:34:28.457369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 301
23.0%
3 173
13.2%
4 158
12.1%
2.5 132
10.1%
1.5 110
 
8.4%
1 81
 
6.2%
5 52
 
4.0%
6 44
 
3.4%
3.5 24
 
1.8%
8 23
 
1.8%
Other values (9) 53
 
4.1%
(Missing) 156
11.9%
ValueCountFrequency (%)
1 81
 
6.2%
1.5 110
 
8.4%
2 301
23.0%
2.5 132
10.1%
3 173
13.2%
3.5 24
 
1.8%
4 158
12.1%
4.5 3
 
0.2%
5 52
 
4.0%
6 44
 
3.4%
ValueCountFrequency (%)
20 1
 
0.1%
18 1
 
0.1%
16 4
 
0.3%
14 4
 
0.3%
12 4
 
0.3%
10 8
 
0.6%
9 9
 
0.7%
8 23
1.8%
7 19
1.5%
6 44
3.4%

coil_length4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)2.8%
Missing315
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean5.7308468
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:28.629252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.875
median4
Q36
95-th percentile15
Maximum50
Range49
Interquartile range (IQR)3.125

Descriptive statistics

Standard deviation5.5854193
Coefficient of variation (CV)0.97462373
Kurtosis11.793346
Mean5.7308468
Median Absolute Deviation (MAD)2
Skewness3.0531117
Sum5685
Variance31.196908
MonotonicityNot monotonic
2023-09-16T15:34:28.806653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2 231
17.7%
4 191
14.6%
3 175
13.4%
6 130
9.9%
8 88
 
6.7%
10 51
 
3.9%
15 26
 
2.0%
30 17
 
1.3%
1 16
 
1.2%
20 14
 
1.1%
Other values (18) 53
 
4.1%
(Missing) 315
24.1%
ValueCountFrequency (%)
1 16
 
1.2%
2 231
17.7%
2.5 1
 
0.1%
3 175
13.4%
3.5 3
 
0.2%
4 191
14.6%
4.5 2
 
0.2%
5 7
 
0.5%
6 130
9.9%
7 2
 
0.2%
ValueCountFrequency (%)
50 1
 
0.1%
40 1
 
0.1%
35 1
 
0.1%
30 17
1.3%
25 8
0.6%
24 1
 
0.1%
22 1
 
0.1%
21 3
 
0.2%
20 14
1.1%
17 1
 
0.1%

coil_size4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)1.9%
Missing315
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean2.9047379
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:28.969408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33.5
95-th percentile6.45
Maximum20
Range19
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation2.0034651
Coefficient of variation (CV)0.68972321
Kurtosis15.752769
Mean2.9047379
Median Absolute Deviation (MAD)0.5
Skewness3.1628126
Sum2881.5
Variance4.0138726
MonotonicityNot monotonic
2023-09-16T15:34:29.275442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 290
22.2%
1.5 129
9.9%
3 122
 
9.3%
2.5 113
 
8.6%
4 101
 
7.7%
1 85
 
6.5%
5 45
 
3.4%
6 32
 
2.4%
3.5 24
 
1.8%
7 18
 
1.4%
Other values (9) 33
 
2.5%
(Missing) 315
24.1%
ValueCountFrequency (%)
1 85
 
6.5%
1.5 129
9.9%
2 290
22.2%
2.5 113
 
8.6%
3 122
9.3%
3.5 24
 
1.8%
4 101
 
7.7%
4.5 1
 
0.1%
5 45
 
3.4%
6 32
 
2.4%
ValueCountFrequency (%)
20 1
 
0.1%
18 1
 
0.1%
16 2
 
0.2%
14 3
 
0.2%
12 2
 
0.2%
10 6
 
0.5%
9 7
 
0.5%
8 10
 
0.8%
7 18
1.4%
6 32
2.4%

coil_length5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)3.0%
Missing477
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean5.3790361
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:29.441017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q36
95-th percentile15
Maximum50
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.3761766
Coefficient of variation (CV)0.9994684
Kurtosis15.154907
Mean5.3790361
Median Absolute Deviation (MAD)2
Skewness3.4193392
Sum4464.6
Variance28.903275
MonotonicityNot monotonic
2023-09-16T15:34:29.610424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 218
16.7%
3 154
 
11.8%
4 154
 
11.8%
6 99
 
7.6%
8 79
 
6.0%
10 31
 
2.4%
15 16
 
1.2%
1 14
 
1.1%
30 13
 
1.0%
12 11
 
0.8%
Other values (15) 41
 
3.1%
(Missing) 477
36.5%
ValueCountFrequency (%)
1 14
 
1.1%
2 218
16.7%
3 154
11.8%
3.5 5
 
0.4%
4 154
11.8%
5 6
 
0.5%
6 99
7.6%
6.6 1
 
0.1%
7 1
 
0.1%
7.5 1
 
0.1%
ValueCountFrequency (%)
50 1
 
0.1%
40 1
 
0.1%
35 1
 
0.1%
30 13
1.0%
25 3
 
0.2%
24 1
 
0.1%
22 3
 
0.2%
21 2
 
0.2%
20 9
0.7%
15 16
1.2%

coil_size5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)2.2%
Missing477
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean2.7391566
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:29.771286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median2
Q33
95-th percentile6
Maximum19
Range18
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.9282289
Coefficient of variation (CV)0.70394987
Kurtosis17.424291
Mean2.7391566
Median Absolute Deviation (MAD)0.5
Skewness3.3829531
Sum2273.5
Variance3.7180668
MonotonicityNot monotonic
2023-09-16T15:34:29.919641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 249
19.1%
1.5 127
 
9.7%
3 100
 
7.7%
1 89
 
6.8%
4 81
 
6.2%
2.5 78
 
6.0%
5 30
 
2.3%
6 21
 
1.6%
3.5 20
 
1.5%
7 8
 
0.6%
Other values (8) 27
 
2.1%
(Missing) 477
36.5%
ValueCountFrequency (%)
1 89
 
6.8%
1.5 127
9.7%
2 249
19.1%
2.5 78
 
6.0%
3 100
7.7%
3.5 20
 
1.5%
4 81
 
6.2%
4.5 2
 
0.2%
5 30
 
2.3%
6 21
 
1.6%
ValueCountFrequency (%)
19 1
 
0.1%
16 3
 
0.2%
14 1
 
0.1%
12 3
 
0.2%
10 3
 
0.2%
9 7
 
0.5%
8 7
 
0.5%
7 8
 
0.6%
6 21
1.6%
5 30
2.3%

coil_count
Real number (ℝ)

Distinct37
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8125478
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2023-09-16T15:34:30.097240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile15
Maximum59
Range58
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.3165547
Coefficient of variation (CV)0.78040622
Kurtosis23.140975
Mean6.8125478
Median Absolute Deviation (MAD)2
Skewness3.5399602
Sum8904
Variance28.265754
MonotonicityNot monotonic
2023-09-16T15:34:30.274736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
4 162
12.4%
3 159
12.2%
5 149
11.4%
6 148
11.3%
7 116
8.9%
2 103
7.9%
8 98
7.5%
9 71
 
5.4%
10 56
 
4.3%
1 53
 
4.1%
Other values (27) 192
14.7%
ValueCountFrequency (%)
1 53
 
4.1%
2 103
7.9%
3 159
12.2%
4 162
12.4%
5 149
11.4%
6 148
11.3%
7 116
8.9%
8 98
7.5%
9 71
5.4%
10 56
 
4.3%
ValueCountFrequency (%)
59 1
0.1%
58 1
0.1%
54 1
0.1%
44 1
0.1%
38 1
0.1%
36 1
0.1%
34 1
0.1%
33 1
0.1%
31 1
0.1%
30 1
0.1%

Aneu_width_label
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
0
852 
1
285 
2
170 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1307
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 852
65.2%
1 285
 
21.8%
2 170
 
13.0%

Length

2023-09-16T15:34:30.451454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:34:30.613577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 852
65.2%
1 285
 
21.8%
2 170
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 852
65.2%
1 285
 
21.8%
2 170
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1307
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 852
65.2%
1 285
 
21.8%
2 170
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1307
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 852
65.2%
1 285
 
21.8%
2 170
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 852
65.2%
1 285
 
21.8%
2 170
 
13.0%

Interactions

2023-09-16T15:34:17.020739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:14.498435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:18.125733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:21.673828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:25.218850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:28.663893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:31.581555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:35.177061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:38.520335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:41.527871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:45.210472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:48.863028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:52.107563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:55.572337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:59.040570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:02.814285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:06.271469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:10.050626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:13.565018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:17.195974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:14.694893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:18.310295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:21.854951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:25.408536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:28.811172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:31.761837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:35.360880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:38.825532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:41.709981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:45.398230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:49.045817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:52.290009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:55.754568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:59.225123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:03.002637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:06.473849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:10.229635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:13.757620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:17.548669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:15.046806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:18.490112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:22.035337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:25.594065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:28.948930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:31.946547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:35.538328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:38.972195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:41.890774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:45.581220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:49.223859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:52.469491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:55.935502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:59.412437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:03.193565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:06.673475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:10.411896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:13.948950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:17.720272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:15.223403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:18.661206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:22.211793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:25.777219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:29.080516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:32.126061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:35.712686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:39.124273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:42.069931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:45.762003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:49.396487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:52.643558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:56.112829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:59.595861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:03.372793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:06.874599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:10.592260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:14.134519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:17.903136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:15.410663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:18.848984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:22.395634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:25.964827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:29.379494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:32.317812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:35.899859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:39.276854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:42.255471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:45.954907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:49.560346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:52.984580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:56.298570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:59.794348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:03.565221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:07.082667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:10.781389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:14.326715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:18.056514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:15.559993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:18.991051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:22.531848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:26.106516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:29.515994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:32.465989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:36.044746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:39.412647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:42.399650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:46.106668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:49.704408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:53.124115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:56.438254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:59.932291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:03.708380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:07.227967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:10.922979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:14.473861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:18.233346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:15.744100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:19.196522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:22.706367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:26.284522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:29.662864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:32.648574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:36.225403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:39.564814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:42.579484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:46.291823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:49.879699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:53.301133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:56.613461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:00.128395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:03.902175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:07.420810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:11.113077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:14.672457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:18.405655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:15.920247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:19.369398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:22.875439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:26.461353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:29.808079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:32.829074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:36.393375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:39.720977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:42.757285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:46.467806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:50.052768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:53.472661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:56.782964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:00.305494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-09-16T15:33:31.425696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:34.999913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:38.343053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:41.365104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:45.037420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:48.678587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:51.935603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:55.402560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:33:58.888240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:02.458332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:06.091778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:09.858681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:13.390420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:16.841762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-16T15:34:30.797970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
IDAgeAneu_neckAneu_widthAneu_depthAneu_heightAneu_volumeVERcoil_length1coil_size1coil_length2coil_size2coil_length3coil_size3coil_length4coil_size4coil_length5coil_size5coil_countSexAneu_locationAdj_techIs_blebAneu_width_label
ID1.0000.043-0.019-0.1050.006-0.167-0.4860.091-0.210-0.227-0.166-0.196-0.179-0.262-0.196-0.321-0.195-0.3410.1220.0000.0920.3190.0890.092
Age0.0431.0000.1620.1050.3130.0970.020-0.2570.0870.0990.0920.0840.0710.0480.0870.0790.0850.0880.0990.1010.1010.0640.0720.080
Aneu_neck-0.0190.1621.0000.7140.7850.626-0.006-0.1260.6220.6450.6240.6240.6290.6030.5920.5640.5800.5270.5670.0760.1820.2040.0000.464
Aneu_width-0.1050.1050.7141.0000.9440.7210.039-0.1870.7920.8280.7720.7880.7760.7770.7520.7590.7200.7240.6660.0000.1210.0810.0550.797
Aneu_depth0.0060.3130.7850.9441.0000.9371.000-0.0960.9220.9410.8870.8970.9090.8860.8640.8480.7760.7560.7470.0000.1750.2730.0000.714
Aneu_height-0.1670.0970.6260.7210.9371.0000.087-0.2920.7920.8350.7620.7760.7440.7430.7110.7250.6730.6850.5810.0000.0730.0870.0480.533
Aneu_volume-0.4860.020-0.0060.0391.0000.0871.000-0.1280.0610.0930.0890.1130.0780.1170.0520.1380.0830.169-0.1140.0000.0000.0300.0000.175
VER0.091-0.257-0.126-0.187-0.096-0.292-0.1281.000-0.158-0.161-0.174-0.170-0.259-0.276-0.204-0.250-0.227-0.2090.1590.0000.0000.0140.0950.000
coil_length1-0.2100.0870.6220.7920.9220.7920.061-0.1581.0000.9360.8370.8420.7950.7930.7530.7530.6920.7170.5290.0000.0910.1110.0100.593
coil_size1-0.2270.0990.6450.8280.9410.8350.093-0.1610.9361.0000.8680.8970.8340.8470.7920.8110.7430.7830.5830.0400.1060.0850.0440.654
coil_length2-0.1660.0920.6240.7720.8870.7620.089-0.1740.8370.8681.0000.9370.8870.8660.8220.8160.7780.7820.5460.0270.1100.0750.0000.598
coil_size2-0.1960.0840.6240.7880.8970.7760.113-0.1700.8420.8970.9371.0000.8920.9150.8390.8590.7890.8200.5710.0000.1160.0640.1070.631
coil_length3-0.1790.0710.6290.7760.9090.7440.078-0.2590.7950.8340.8870.8921.0000.9250.9000.8560.8410.8190.5250.0570.1260.0620.0960.576
coil_size3-0.2620.0480.6030.7770.8860.7430.117-0.2760.7930.8470.8660.9150.9251.0000.8810.9180.8350.8640.5450.0560.1160.0740.1150.610
coil_length4-0.1960.0870.5920.7520.8640.7110.052-0.2040.7530.7920.8220.8390.9000.8811.0000.9000.9110.8520.4940.0000.1050.0510.1020.564
coil_size4-0.3210.0790.5640.7590.8480.7250.138-0.2500.7530.8110.8160.8590.8560.9180.9001.0000.8690.9280.5120.0510.1060.0100.1010.579
coil_length5-0.1950.0850.5800.7200.7760.6730.083-0.2270.6920.7430.7780.7890.8410.8350.9110.8691.0000.8800.4630.0730.0940.0000.1080.519
coil_size5-0.3410.0880.5270.7240.7560.6850.169-0.2090.7170.7830.7820.8200.8190.8640.8520.9280.8801.0000.4740.0160.1100.0000.1040.527
coil_count0.1220.0990.5670.6660.7470.581-0.1140.1590.5290.5830.5460.5710.5250.5450.4940.5120.4630.4741.0000.0000.0910.1340.1120.408
Sex0.0000.1010.0760.0000.0000.0000.0000.0000.0000.0400.0270.0000.0570.0560.0000.0510.0730.0160.0001.0000.2970.1030.0000.000
Aneu_location0.0920.1010.1820.1210.1750.0730.0000.0000.0910.1060.1100.1160.1260.1160.1050.1060.0940.1100.0910.2971.0000.1980.0000.148
Adj_tech0.3190.0640.2040.0810.2730.0870.0300.0140.1110.0850.0750.0640.0620.0740.0510.0100.0000.0000.1340.1030.1981.0000.0000.126
Is_bleb0.0890.0720.0000.0550.0000.0480.0000.0950.0100.0440.0000.1070.0960.1150.1020.1010.1080.1040.1120.0000.0000.0001.0000.046
Aneu_width_label0.0920.0800.4640.7970.7140.5330.1750.0000.5930.6540.5980.6310.5760.6100.5640.5790.5190.5270.4080.0000.1480.1260.0461.000

Missing values

2023-09-16T15:34:20.640969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-16T15:34:21.164977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-16T15:34:21.540913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDSexAgeAneu_locationAneu_neckAneu_widthAneu_depthAneu_heightAneu_volumeAdj_techIs_blebVERcoil_length1coil_size1coil_length2coil_size2coil_length3coil_size3coil_length4coil_size4coil_length5coil_size5coil_countAneu_width_label
03026woman47ICA4.08.08.008.0267.946667BATnoNaN20.08.010.06.010.06.010.04.08.03.062
13027woman79BA3.07.08.5010.0311.383333SimplenoNaN20.08.010.06.010.05.010.05.08.04.062
23030woman54ICA3.05.05.005.065.416667BATnoNaN6.03.02.02.0NaNNaNNaNNaNNaNNaN21
33031man60ACA2.54.54.755.055.931250SimplenoNaN8.04.04.02.5NaNNaNNaNNaNNaNNaN20
43032woman70ICA3.512.012.7513.51080.945000SimplenoNaN30.012.030.010.030.08.030.07.012.05.072
53034woman50ICA3.04.55.005.564.762500SimplenoNaN10.05.08.03.06.03.02.02.0NaNNaN40
63035woman63ICA3.53.54.505.545.333750SimplenoNaN8.04.04.02.53.02.0NaNNaNNaNNaN30
73037woman82ACA3.66.05.805.6101.987200SimplenoNaN10.05.08.04.06.02.5NaNNaNNaNNaN31
83038woman72ICA2.53.03.504.021.980000SimplenoNaN6.03.03.02.02.02.01.02.0NaNNaN40
93039woman72ICA4.05.06.007.0109.900000SimplenoNaN15.07.010.05.08.04.08.04.08.03.071
IDSexAgeAneu_locationAneu_neckAneu_widthAneu_depthAneu_heightAneu_volumeAdj_techIs_blebVERcoil_length1coil_size1coil_length2coil_size2coil_length3coil_size3coil_length4coil_size4coil_length5coil_size5coil_countAneu_width_label
12974422man53ACA3.234.07NaN2.450.0Stent assistnoNaN8.03.54.02.03.02.03.01.53.01.560
12984423woman78MCA2.312.97NaN2.830.0Stent assistnoNaN8.03.54.02.53.02.03.01.0NaNNaN40
12994424woman52MCA2.712.54NaN3.310.0Stent assistnoNaN4.02.54.02.03.01.03.01.03.01.060
13004425woman40ICA2.954.88NaN4.490.0Stent assistnoNaN8.03.510.03.04.02.53.01.53.01.580
13014427woman63MCA2.973.58NaN1.750.0Stent assistnoNaN4.02.56.03.04.02.03.01.5NaNNaN40
13024430man45MCA1.883.16NaN4.100.0SimplenoNaN8.03.54.02.52.01.02.01.02.01.060
13034431woman29ICA2.993.92NaN2.990.0Stent assistnoNaN6.03.04.02.0NaNNaNNaNNaNNaNNaN20
13044432woman49MCA2.503.00NaN4.000.0Double cathenoNaN6.03.03.02.02.02.02.02.0NaNNaN40
13054433woman50ICA1.503.00NaN3.500.0BATnoNaN6.03.02.01.52.01.52.01.5NaNNaN40
13064434woman60VA6.007.50NaN8.500.0Stent assistnoNaN30.010.020.08.015.07.015.07.010.06.0142